CN110084830A - A kind of detection of video frequency motion target and tracking - Google Patents
A kind of detection of video frequency motion target and tracking Download PDFInfo
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Abstract
The present invention proposes a kind of detection of video frequency motion target and tracking, collects image to video capture device first and pre-processes, and every several frame images, just establishes background model using gauss hybrid models；The foreground image of current frame image is extracted according to established background model for current frame image；And ORB characteristic point is extracted to current frame image；Canny edge detection is done to current frame image later, edge is connected with dual-threshold voltage, carries out corrosion expansion process when necessary, obtains several quasi- targets with complete edge profile；Alignment target is given a mark using evaluation function, using the maximum quasi- target of evaluation index as moving target；Using KCF algorithm keeps track target, if target is lost, carries out ORB feature and match reacquisition target again.Invention achieves the purposes of moving object detection and tracking rapidly and efficiently realized.
Description
Technical field
The present invention relates to technical field of information processing, specially a kind of video frequency motion target detection and tracking.
Background technique
Video object detection and tracking technology is all applied in many fields, is certain in following development constantly
The needs of improving to meet us.Either military affairs on apply Missile Terminal Guidance video guidance phases, optical tracking instrument and
The automatic identification in assembly line, industrial robot and industrial hazard invasion inspection are applied in infrared small target detection tracking in the industry
Survey etc., or apply in life in intelligent video monitoring, vehicle-carried tracking instrument and video conference etc..Target detection in these areas
All be with tracking technique we provide conveniently, safely, fast with intelligentized guarantee, live in us more intelligent,
In the environment of safe.
Video frequency motion target detection technique detects the mobile target that we are of interest in video.Moving object detection skill
Art was not only basic but also difficult.From the point of view of the motility of video source, moving object detection can be divided into two kinds: background changes and back
Scape is constant.The fixed mobile target of the constant i.e. video source of background, the relatively small comparative maturity of track algorithm difficulty, background variation
Moving target detection i.e. under mobile platform, the continuous renewal as background or the noise jamming as caused by DE Camera Shake,
Increase algorithm difficulty in this case significantly.
Currently, for the side of many researcher's Bring out Background penalty methods of target detection technique under background change condition
Method.Background compensation method eliminates camera motion bring global motion by coordinate transform, then compensates background and does difference again, this
There are two problems for kind method, first is that background compensation is inaccurate, work well to the case where background translation, but in background rotation or scape
It is difficult to realize in the case where deep variation；Second is that operations for motion compensation complexity is high, accumulated error is had resulted in.
Summary of the invention
In view of the problems of the existing technology, the present invention proposes a kind of detection of video frequency motion target and tracking, is based on
Background modeling and characteristic point edge constraint realize joint-detection, and match realization tracking again based on feature, to overcome background modeling
Error and target lose problem, reach moving object detection and tracking rapidly and efficiently realization.
Moving object detection and tracking of the invention includes two problems: the target detection problems and moving target of movement
Tracking problem.
For target detection problems: detecting that we are closed in the one section of video or image sequence for having complicated background
The moving target of note needs that background interference is inhibited to remove background, distinguishes target and background.It establishes and mixes for each pixel
Gauss model and model parameter is updated using On-line Estimation method, which region ADAPTIVE MIXED Gauss model may determine that
Background may more be belonged to.Then using speed and the excellent ORB characteristic point of accuracy, with image border profile come to carrying out height
Image after the processing of this mixed model and feature constraint does space constraint, is further processed to obtain moving target.
For Target Tracking Problem: carrying out that a filter can be constructed when target following, for distinguishing background and target.With
Filter detection target whether there is in video frame close-proximity target zone search window.It is instructed when constructing filter
Practice, the selection of training sample removes other regions of selected target frame as negative sample using present frame target area as positive sample
This, when the coordinate of other pixels and the distance of target frame are smaller, which is that the probability of positive sample is bigger.And correlation filtering method
Start to be applied to Signal and Information Processing direction, is expanded be applied to Data Detection and identification later.Correlation filtering is applied to
It is due to correlation in terms of target tracking is a measurement for measuring two signal similarities, just as the similitude in mathematics, when
Its smaller similitude of the gap of two signals is higher.In target tracking domain, can be used for measuring former frame and present frame target and
The similarity for predicting target, to reach tracking purpose.This just needs to design a Filtering Template, so that it acts on tracking
Prediction target area obtains maximum response, and the position of maximum value is exactly the position of target.
The technical solution of the present invention is as follows:
A kind of video frequency motion target detection and tracking, it is characterised in that: the following steps are included:
Step 1: each frame image in the collected vision signal of video capture device being pre-processed, including color
Space conversion and filtering；For just being built using gauss hybrid models by pretreated vision signal every several frame images
Vertical background model；
Step 2: the foreground image of current frame image is extracted according to established background model for current frame image；
Step 3: ORB characteristic point is extracted to current frame image；
Step 4: Canny edge detection being done to current frame image, fine edge is removed with dual-threshold voltage and connects edge；
The edge of current frame image can not be fully connected according to dual-threshold voltage, then corrosion expansion process be used to current frame image,
The cavity in current frame image is removed, edge connection is realized, obtains several quasi- targets with complete edge profile；
Step 5: being given a mark using evaluation function to the quasi- target that step 4 obtains, evaluation index is
Wherein m indicates m-th of quasi- target, Sm,0Indicate the number of pixels in m-th of quasi- target with prospect value, Sm,inTable
Show the total pixel number in m-th of quasi- target, nmIndicate the ORB feature point number in m-th of quasi- target；If all quasi- objective appraisals
Index is respectively less than threshold value, then it is assumed that present frame does not have moving target, carries out operation to next frame return step 2 again；If there is
Quasi- objective appraisal index is greater than threshold value, then using the maximum quasi- target of evaluation index as moving target；
Step 6: the moving target identified target collimation mark being remembered, sets matching template for target frame, and store
The Feature Descriptor information of moving target ORB characteristic point updates the moving target ORB characteristic point spy once stored every setting frame
Sign description sub-information, use when to mark loss；
Step 7: using KCF algorithm keeps track target, if target is lost, carry out ORB feature and match reacquisition target again.
Further preferred embodiment, a kind of video frequency motion target detection and tracking, it is characterised in that: step 7
In when using KCF algorithm keeps track object procedure, the variation of target scale is tracked using multiple dimensioned KCF, by using than
Current size larger and smaller size detects, and comparison match peak of function carries out size adjusting.
Further preferred embodiment, a kind of video frequency motion target detection and tracking, it is characterised in that: step 7
In, if KCF peak function cycle detection maximum value is less than threshold value again, it is believed that target is lost, then carries out ORB feature and match to obtain again
Take target.
Beneficial effect
Compared with the prior art, the present invention has the following advantages:
First, relative to traditional target detection and tracking, the present invention proposes new algorithm of target detection and target
Track algorithm, the realization of algorithm improve detecting and tracking efficiency；
Second, the present invention proposes the matched mode of feature weight for Target Tracking Problem, increases the robustness of system；
Third, the present invention are improved and are mended aiming at the problem that background modeling method is easy the foreground target of detection mistake
It repays, so that moving object detection is more accurate.
Additional aspect and advantage of the invention will be set forth in part in the description, and will partially become from the following description
Obviously, or practice through the invention is recognized.
Detailed description of the invention
Above-mentioned and/or additional aspect of the invention and advantage will become from the description of the embodiment in conjunction with the following figures
Obviously and it is readily appreciated that, in which:
Fig. 1 is implementation flow chart of the invention；
Fig. 2 is the implementation flow chart with the present invention to target detection；
Fig. 3 is the flow chart with the present invention to multiscale target tracking.
Specific embodiment
The embodiment of the present invention is described below in detail, the embodiment is exemplary, it is intended to it is used to explain the present invention, and
It is not considered as limiting the invention.
The present invention proposes a kind of detection of video frequency motion target and tracking, and wherein background modeling method is good fast using effect
It spends fast mixed Gauss model and does foreground detection.ORB characteristic point is chosen as video object and detects characteristic point, utilizes gradient edge
Joint-detection is carried out as space constraint.When to unknown Moving target detection, first transported using mixed Gauss model Preliminary detection
Moving-target, since mixed Gauss model testing result can be because of ambient noise caused by camera motion, at this point, binding characteristic point is to movement
Moving target is screened in object area detection.For cope with motion target tracking dimensional variation and occlusion issue, using spy
Sign point weight matching tracking method FP-KCF.Assuming that the tracing positional of former frame is accurate and target trajectory is smooth
, then local window search is executed near the future position of present frame, the step-size in search that computing capability allows is for obtaining
Maximum speed, it is sufficiently small to improve matching precision.In order to not influence tracking velocity, using 3 scales, by using than current ruler
Very little larger and smaller size detects, and comparison match peak of function carries out size adjusting.Target is lost, is used
ORB feature matches again reacquires target.
As shown in Figure 1, the specific implementation step in the present embodiment are as follows:
Step 1: the collected target scene vision signal of video capture device inputs target detection by USB interface in real time
Tracking system pre-processes each frame image in target scene vision signal, converts collected yuv video to
RGB three-channel video, and use bilateral filtering to figure smoothing processing with Protect edge information；For believing by pretreated video
Number, every 2 frame images, background model just is established using gauss hybrid models；
Step 2: the foreground image of current frame image being extracted according to established background model for current frame image: will
Foreground pixel point pixel value is set to 0, and background pixel value is set to 1, to obtain prospect binary map；
Step 3: ORB characteristic point being extracted to current frame image: current frame image ORB characteristic point is calculated, by characteristic point picture
Plain value is set to 0, and non-characteristic point pixel value is set to 1, to obtain characteristic point binary map；
Step 4: Canny edge detection being done to current frame image, fine edge is removed with dual-threshold voltage and connects edge；
The edge of current frame image can not be fully connected according to dual-threshold voltage, then corrosion expansion process be used to current frame image,
The cavity in current frame image is removed, edge connection is realized, obtains several quasi- targets with complete edge profile；
Step 5: being given a mark using evaluation function to the quasi- target that step 4 obtains, evaluation index is
Wherein m indicates m-th of quasi- target, Sm,0Indicate the number of pixels in m-th of quasi- target with prospect value, Sm,inTable
Show the total pixel number in m-th of quasi- target, nmIndicate the ORB feature point number in m-th of quasi- target；If all quasi- objective appraisals
Index is respectively less than threshold value, then it is assumed that present frame does not have moving target, carries out operation to next frame return step 2 again；If there is
Quasi- objective appraisal index is greater than threshold value, then using the maximum quasi- target of evaluation index as moving target；
Step 6: the moving target identified target collimation mark being remembered, sets matching template for target frame, and store
The Feature Descriptor information of moving target ORB characteristic point updates the moving target ORB feature point feature once stored every 5 frames
Sub-information, use when to mark loss are described；
Step 7: KCF algorithm keeps track target is used, if KCF peak function cycle detection maximum value is less than threshold value, it is believed that mesh
Mark is lost, then carries out ORB feature and match reacquisition target again: comparing the Hamming of two characteristic point Feature Descriptor character arrays
Distance, i.e., different the sum of the digit of numerical value in same position, Hamming distance is smaller more to be matched.Select the Chinese of matched point pair
Prescribed distance is less than twice of minimum range as judgment basis, is then considered a wrong matching if it is less than the value, filters
Fall；A correctly matching is then considered greater than the value.Target is found if meeting matching condition, is otherwise more than weight match time
Think that target is lost, program determination operation.
Referring to Fig. 2, the implementation process of target detection are as follows:
Background modeling uses mixed Gauss model, and several common Feature Points Extractions are compared when selecting characteristic point
Performance.Using the ORB characteristic point of function admirable, connect to be formed completely for removal cavity, edge when doing rim space constraint
Edge binary map carries out corrosion dilation operation and denoises operation.Score is calculated according to evaluation function in evaluation index to obtain to the end
Motion target area, judges whether target area meets minimal characteristic points, if being unsatisfactory for extracting current frame image again does weight
Multiple judgement.
Referring to Fig. 3, the process of multiscale target tracking are as follows:
According to current frame image and target position and size, the response such as formula of regression is utilized
With the rapid computations such as formula of Gaussian kernel
Obtain target response result and value；Then next frame image is read, is made the difference using amplitude to position the position of peak value,
What is returned is that the offset size changed is needed to determine best match target's center position；It is first found out with archeus and quickly to be detected
0.95 times of response results peak value and previous frame tracking result is made comparisons.Here taking 0.95 is due to detecting in other scales
When, in order to increase the stability of system, certain decaying is done in parent peak value.If peak value of response is greater than 0.95T and illustrates prediction bits
Scale is set less than or equal to current scale, selects small size measurement, wherein 0.005,0.01 etc. can be chosen for dimensional variation step-length,
It is more bigger more accurate to be worth smaller operand, otherwise convergence ratio is comparatively fast inaccurate.It is taken herein to adapt to the rate request on ARM
0.01 step-length.Above procedure is repeated, is often repeated once, has reached scale minimum value until peak value of response is greater than 0.95T explanation,
Updating scale is that detection next time is prepared.If scale increases, i.e., the peak value of response of prediction result is less than 0.95T and illustrates predicted position
Scale is greater than current scale, needs that bigger scale is selected to detect, until being greater than 0.95T until detecting peak value.
Although the embodiments of the present invention has been shown and described above, it is to be understood that above-described embodiment is example
Property, it is not considered as limiting the invention, those skilled in the art are not departing from the principle of the present invention and objective
In the case where can make changes, modifications, alterations, and variations to the above described embodiments within the scope of the invention.
Claims (3)
1. a kind of video frequency motion target detection and tracking, it is characterised in that: the following steps are included:
Step 1: each frame image in the collected vision signal of video capture device being pre-processed, including color space
Conversion and filtering；For passing through pretreated vision signal, every several frame images, back just is established using gauss hybrid models
Scape model；
Step 2: the foreground image of current frame image is extracted according to established background model for current frame image；
Step 3: ORB characteristic point is extracted to current frame image；
Step 4: Canny edge detection being done to current frame image, fine edge is removed with dual-threshold voltage and connects edge；If adopting
The edge of current frame image can not be fully connected with dual-threshold voltage, then to current frame image using corrosion expansion process, removal
Cavity in current frame image realizes edge connection, obtains several quasi- targets with complete edge profile；
Step 5: being given a mark using evaluation function to the quasi- target that step 4 obtains, evaluation index is
Wherein m indicates m-th of quasi- target, Sm,0Indicate the number of pixels in m-th of quasi- target with prospect value, Sm,inIndicate m
Total pixel number in a quasi- target, nmIndicate the ORB feature point number in m-th of quasi- target；If all quasi- objective appraisal indexs
Respectively less than threshold value, then it is assumed that present frame does not have moving target, carries out operation to next frame return step 2 again；If there is quasi- mesh
It marks evaluation index and is greater than threshold value, then using the maximum quasi- target of evaluation index as moving target；
Step 6: the moving target identified target collimation mark being remembered, sets matching template for target frame, and store movement
The Feature Descriptor information of target ORB characteristic point updates the moving target ORB feature point feature once stored every setting frame and retouches
Sub-information is stated, use when to mark loss；
Step 7: using KCF algorithm keeps track target, if target is lost, carry out ORB feature and match reacquisition target again.
2. a kind of video frequency motion target detection and tracking according to claim 1, it is characterised in that: make in step 7
When with KCF algorithm keeps track object procedure, the variation of target scale is tracked using multiple dimensioned KCF, by using than current ruler
Very little larger and smaller size detects, and comparison match peak of function carries out size adjusting.
3. a kind of video frequency motion target detection and tracking according to claim 1, it is characterised in that: in step 7, if
KCF peak function cycle detection maximum value is less than threshold value, it is believed that target is lost, then carries out ORB feature and match reacquisition mesh again
Mark.
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| CN111242981A (en) * | 2020-01-21 | 2020-06-05 | 北京捷通华声科技股份有限公司 | Tracking method and device for fixed object and security equipment |
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| CN115170792A (en) * | 2022-09-07 | 2022-10-11 | 烟台艾睿光电科技有限公司 | Infrared image processing method, device and equipment and storage medium |
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